In this No Black Box Machine Learning Course in JavaScript, you will gain a deep understanding of machine learning systems by coding without relying on libraries. This unique approach not only demystifies the inner workings of machine learning but also significantly enhances software development skills.
โ๏ธ Course created by @Radu (PhD in Computer Science)
๐ฅ Watch part two: https://youtu.be/3wwiOSxDAmg
HOMEWORK
๐ 1st assignment spreadsheet: https://docs.google.com/spreadsheets/d/16wIddJ9jKAAvJOXPcF0gNRx39AOE9A2-mQeK6UR2qnY/edit?usp=sharing
๐ Submit all other assignments to Radu’s Discord Server: https://discord.com/invite/gJFcF5XVn9
GITHUB LINKS
๐ป Drawing App: https://github.com/gniziemazity/drawing-app
๐ป Data: https://github.com/gniziemazity/drawing-data
๐ป Custom Chart Component: https://github.com/gniziemazity/javascript_chart
๐ป Full Course Code (In Parts): https://github.com/gniziemazity/ml-course
PREREQUISITES
๐ฅ Interpolation: https://youtu.be/J_puRs40GhM
๐ฅ Linear Algebra: https://youtu.be/nzyOCd9FcCA
๐ฅ Trigonometry: https://youtu.be/xK3vKWMFVgw
LINKS
๐ Check out the Recognizer we’ll build in this course: https://radufromfinland.com/projects/ml/recognizer
๐ Draw for Radu, Call for help video: https://youtu.be/Yw2QZ1vq2ek
๐ Draw for Radu, Data collection tool: https://radufromfinland.com/projects/ml
๐ Radu’s Self-driving Car Course: https://www.youtube.com/playlist?list=PLB0Tybl0UNfYoJE7ZwsBQoDIG4YN9ptyY
๐ Radu’s older Machine Learning video: https://youtu.be/QXB1ytG95gs
๐ CHART TUTORIAL (mentioned at 01:45:27): https://youtu.be/n8uCt1TSGKE
๐ CHART CODE: https://github.com/gniziemazity/javascript_chart
TOOLS
๐ง Visual Studio Code: https://code.visualstudio.com/download
๐ง Google Chrome: https://www.google.com/chrome
๐ง Node JS: https://nodejs.org/en/download
(make sure you add ‘node’ and ‘npm’ to the PATH environment variables when asked!)
TIMESTAMPS
โจ๏ธ(0:00:00) Introduction
โจ๏ธ(0:05:04) Drawing App
โจ๏ธ(0:46:46) Homework 1
โจ๏ธ(0:47:05) Working with Data
โจ๏ธ(1:08:54) Data Visualizer
โจ๏ธ(1:29:52) Homework 2
โจ๏ธ(1:30:05) Feature Extraction
โจ๏ธ(1:38:07) Scatter Plot
โจ๏ธ(1:46:12) Custom Chart
โจ๏ธ(2:01:03) Homework 3
โจ๏ธ(2:01:35) Nearest Neighbor Classifier
โจ๏ธ(2:43:21) Homework 4 (better box)
โจ๏ธ(2:43:53) Data Scaling
โจ๏ธ(2:54:45) Homework 5
โจ๏ธ(2:55:23) K Nearest Neighbors Classifier
โจ๏ธ(3:04:18) Homework 6
โจ๏ธ(3:04:49) Model Evaluation
โจ๏ธ(3:21:29) Homework 7
โจ๏ธ(3:22:01) Decision Boundaries
โจ๏ธ(3:39:26) Homework 8
โจ๏ธ(3:39:59) Python & SkLearn
โจ๏ธ(3:50:35) Homework 9
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